PDF(Pubmed)
Abstract：
The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient\'s presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model\'s predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.
摘要：
这项研究的目的是开发临床预测工具，以指导在癫痫发作的非创伤性泰国患者中使用计算机断层扫描（CT）头部扫描。使用回顾性横截面设计进行了预测模型。我们招募了被医生诊断为癫痫发作并接受CT头部扫描的成年患者（年龄≥18岁）进行进一步调查。阳性CT头为放射科医生正式报告的与患者出现癫痫相关的任何新病变。总共9个候选预测因子被预选。预测模型是使用具有向后逐步消除的全多变量逻辑回归开发的。我们通过AuROC和校准图评估了模型的预测性能，包括其辨别能力和校准。然后基于最终模型构建应用程序。共有362例患者纳入分析，其中71例CT头颅检查结果阳性，291例结果正常。确定了六个最终预测因子，包括：格拉斯哥昏迷量表，局灶性神经功能缺损的存在，恶性肿瘤病史，CVA的历史，癫痫,和酒精戒断症状的存在。在辨别能力方面，最终模型表现出优异的性能(AuROC为0.82(95%CI:0.76-0.87))。校准图说明了观察到的和预测的风险之间的良好一致性。该预测模型提供了一种可靠的工具，可有效减少不必要的使用，并为支持医生确定非创伤性癫痫患者是否需要进行CT头部扫描提供信心。
